A AI Blueprint is a consistent, machine learning (ML) pipeline built on reusable blocks of code created by domain experts and deployable by anyone. The platform provides open-source, pre-assembled blueprints that can be leveraged by software developers and business users who want to apply ML technology to enhance their applications and businesses in a simplified manner.

Whether you’re an AI beginner, a technical expert, or a business leader assessing technology fits for your organization, AI Blueprints offer an excellent starting point for your AI application journey. Each AI Blueprint has been carefully crafted to implement an ML use-case that supports simple custom modifications to bring your data into the pipeline.

The platform hosts a Marketplace of AI Blueprints and associated components to simplify the building of ML pipelines, and can be fully integrated with popular third-party services and frameworks. Head over to the Marketplace to learn more and find the right set of integrations to meet your requirements. If you cannot find a particular integration, please reach out to one of the experts at the support portal to make a suggestion.

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At a high level, AI Blueprints are organized by their role in the AI pipeline and the ML task they perform, such as Object Detection or Sentiment Analysis. The three main building blocks within an AI pipeline are, Data Connectors, Models, Deployment Options.

These tasks are further organized into workflows that are either training-focused or inference-focused. Additionally, provides a rich set of integration connectors to provide flexibility in connecting AI Blueprints with third-party applications and services. We also provide an easy-to-use, Try it Live feature for users to get a quick understanding of the type of results that can be expected out of an AI Blueprint. Comprehensive documentation accompanies each AI Blueprint, explaining its structure, usage, and purpose, including useful code snippets to help users understand ML concepts and apply training and inference processes.

Please refer to the available AI Blueprint workshop sessions and hands-on guides to learn more. Head over to the Metacloud Blueprints page to get started deploying your first AI Blueprint in a matter of minutes!

Training Blueprint

A training AI Blueprint focuses on determining model weights and convergence toward a specific ML use case This is ideal if users would like to fine-tune a model and train it on proprietary data that fits a unique use-case. For example, an e-commerce application might want to use sales-data to create a unique recommender system to facilitate a better shopping experience for customers.

To access the training-related AI Blueprints, log in to the Metacloud, click the Blueprints tab on the left sidebar to display the AI Blueprints page, and click the Training button at the top. Training Blueprints can also be accessed by simply navigating to the Metacloud website and clicking the top Training button.

Both actions display all the AI Blueprints experts have carefully crafted to guide users toward their model-training goals. Reference one of the hands-on workshop sessions or follow a specific step-by-step AI Blueprint guide to experiment at your own pace with your own data.


As an example, let’s walk through how an AI Blueprint is used. Select the popular Training AI Blueprint called Object Detection.


Selecting Object Detection Training displays its page containing information about the AI Blueprint and the tasks comprising its pipeline. This page explains the purpose of the AI Blueprint, provides a visual diagram of the task sequence, and lists sequential instructions to help users train the model and deploy the endpoint within their computing environment. The bottom of the page displays the Connected Libraries section, which includes the Integration Connectors required to run the AI Blueprint.